code: def fit(self, X, y):
      from sklearn.ensemble import RandomForestClassifier
  
      self.n_estimators = int(self.n_estimators)
  
      if self.max_features not in ("sqrt", "log2", "auto"):
          max_features = int(X.shape[1] ** float(self.max_features))
      else:
          max_features = self.max_features
  
      self.estimator = RandomForestClassifier(
          n_estimators=self.n_estimators,
          max_features=max_features,
          random_state=self.random_state,
      )
      self.estimator.fit(X, y)
      return self
  
file_path: auto-sklearn/examples/80_extending/example_restrict_number_of_hyperparameters.py
imports: from typing import Optional
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformIntegerHyperparameter, UniformFloatHyperparameter
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import train_test_split
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  import autosklearn.classification
  import autosklearn.pipeline.components.classification
  from autosklearn.pipeline.components.classification import AutoSklearnClassificationAlgorithm
  from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA, PREDICTIONS, SPARSE
label_desc: Use image node_id 2 for calling the CustomRandomForest obj's underlying member method code with example usage: obj.fit(X, y) and returns: self